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test-mul-mat.cpp
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test-mul-mat.cpp
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#include "ggml.h"
#include "ggml/ggml-alloc.h"
#include "ggml/ggml-backend.h"
//#define GGML_USE_CUBLAS // uncomment this to use cuda backend, make sure build ggml lib with GGML_CUBLAS=ON
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
struct test_model {
struct ggml_tensor * a;
struct ggml_tensor * b;
ggml_backend_t backend = NULL;
ggml_backend_buffer_t buffer;
struct ggml_context * ctx;
};
void load_model(test_model & model, float* a, float* b, int M, int N, int K, bool use_gpu = false) {
size_t buffer_size = 0;
{
buffer_size += (M * N) * ggml_type_size(GGML_TYPE_F32); // tensor a
buffer_size += (N * K) * ggml_type_size(GGML_TYPE_F32); // tensor b
buffer_size += 1024; // overhead
}
printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
printf("%s: backend buffer size = %d bytes\n", __func__, (int) buffer_size);
int num_tensors = 2;
struct ggml_init_params params {
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
// initialize the backend
#ifdef GGML_USE_CUBLAS
if (use_gpu) {
fprintf(stderr, "%s: using CUDA backend\n", __func__);
model.backend = ggml_backend_cuda_init(0);
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
}
#endif
#ifdef GGML_USE_METAL
if (use_gpu) {
fprintf(stderr, "%s: using Metal backend\n", __func__);
model.backend = ggml_backend_metal_init();
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
}
#endif
if(!model.backend) {
// fallback to CPU backend
model.backend = ggml_backend_cpu_init();
}
model.buffer = ggml_backend_alloc_buffer(model.backend, buffer_size);
// create context
model.ctx = ggml_init(params);
// create tensors
model.a = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, K, M);
printf("Matrix A: [%i, %i]\n", K, M);
model.b = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, K, N);
printf("Matrix B: [%i, %i]\n", K, N);
// create a allocator
struct ggml_tallocr alloc = ggml_tallocr_new(model.buffer);
// alloc memory
ggml_tallocr_alloc(&alloc, model.a);
// load data to buffer
if(ggml_backend_is_cpu(model.backend)
#ifdef GGML_USE_METAL
|| ggml_backend_is_metal(model.backend)
#endif
) {
memcpy(model.a->data, a, ggml_nbytes(model.a));
} else {
ggml_backend_tensor_set(model.a, a, 0, ggml_nbytes(model.a)); // cuda requires copy the data directly to device
}
// alloc memory
ggml_tallocr_alloc(&alloc, model.b);
if(ggml_backend_is_cpu(model.backend)
#ifdef GGML_USE_METAL
|| ggml_backend_is_metal(model.backend)
#endif
) {
memcpy(model.b->data, b, ggml_nbytes(model.b));
} else {
ggml_backend_tensor_set(model.b, b, 0, ggml_nbytes(model.b)); // cuda requires copy the data directly to device
}
}
struct ggml_cgraph * build_graph(const test_model& model) {
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
static std::vector<uint8_t> buf(buf_size);
struct ggml_init_params params0 = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf.data(),
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
// create a temporally context to build the graph
struct ggml_context * ctx0 = ggml_init(params0);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
// zT = x @ yT
struct ggml_tensor * result = ggml_mul_mat(ctx0, model.a, ggml_cont(ctx0, model.b));
// z = (zT)T
ggml_build_forward_expand(gf, ggml_cont(ctx0, ggml_transpose(ctx0, result)));
// delete the temporally context used to build the graph
ggml_free(ctx0);
return gf;
}
struct ggml_tensor* compute(const test_model & model, ggml_gallocr_t allocr) {
struct ggml_cgraph * gf = build_graph(model);
// allocate tensors
ggml_gallocr_alloc_graph(allocr, gf);
int n_threads = 1;
if (ggml_backend_is_cpu(model.backend)) {
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
}
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(model.backend)) {
ggml_backend_metal_set_n_cb(model.backend, n_threads);
}
#endif
ggml_backend_graph_compute(model.backend, gf);
//ggml_graph_print(gf);
// in this case, the output tensor is the last one in the graph
return gf->nodes[gf->n_nodes - 1];
}
static void ggml_vec_dot_f16(const int n, float * s, float * x, float * y) {
float sumf = 0.0;
for (int i = 0; i < n; ++i) {
sumf += x[i] * y[i];
}
*s = sumf;
}
static void gemm_f16_out_f32(int m, int n, int k,
float * A,
float * B,
float * C,
const int ith, const int nth) {
// does not seem to make a difference
int m0, m1, n0, n1;
// patches per thread
if (m > n) {
n0 = 0;
n1 = n;
// total patches in dst
const int np = m;
// patches per thread
const int dp = (np + nth - 1)/nth;
// patch range for this thread
m0 = dp*ith;
m1 = std::min(m0 + dp, np);
} else {
m0 = 0;
m1 = m;
// total patches in dst
const int np = n;
// patches per thread
const int dp = (np + nth - 1)/nth;
// patch range for this thread
n0 = dp*ith;
n1 = std::min(n0 + dp, np);
}
// block-tiling attempt
int64_t blck_n = 16;
int64_t blck_m = 16;
for (int j = n0; j < n1; j+=blck_n) {
for (int i = m0; i < m1; i+=blck_m) {
// printf("i j k => %d %d %d\n", i, j, K);
for (int ii = i; ii < i + blck_m && ii < m1; ii++) {
for (int jj = j; jj < j + blck_n && jj < n1; jj++) {
ggml_vec_dot_f16(k,
C + ii*n + jj,
A + ii * k,
B + jj * k);
}
}
}
}
}
void perform_gemm_test(float* a, float* b, float* expected, int M, int N, int K) {
printf("\nPerforming gemm_f16_out_f32 test:\n");
float* gemm_out = new float[M * N];
gemm_f16_out_f32(M, N, K, a, b, gemm_out, 0, 1);
for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
printf("%.1ff,", gemm_out[i * N + j]);
}
printf("\n");
}
bool passed = true;
for(int i = 0; i < M * N; i++) {
if(gemm_out[i] != expected[i]) {
passed = false;
break;
}
}
printf("gemm_mult (%i): %s\n", (M * N), passed ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
}
int main(void)
{
ggml_time_init();
const int M = 4, N = 16, K = 36; // a conv2d expected matrix multiplication
// matrix A (4 X 36)
float matrixA[M * K] = {
2.0f, 9.0f, 2.0f, 10.0f, 6.0f, 4.0f, 3.0f, 6.0f, 3.0f, 6.0f, 9.0f, 7.0f, 8.0f, 8.0f, 3.0f, 3.0f, 10.0f, 5.0f, 2.0f, 10.0f, 7.0f, 10.0f, 9.0f, 3.0f, 6.0f, 6.0f, 5.0f, 10.0f, 2.0f, 3.0f, 6.0f, 1.0f, 9.0f, 4.0f, 10.0f, 4.0f,
10.0f, 7.0f, 8.0f, 10.0f, 10.0f, 8.0f, 7.0f, 10.0f, 4.0f, 6.0f, 8.0f, 7.0f, 7.0f, 6.0f, 9.0f, 3.0f, 6.0f, 5.0f, 5.0f, 2.0f, 7.0f, 2.0f, 7.0f, 4.0f, 4.0f, 6.0f, 6.0f, 4.0f, 3.0f, 9.0f, 3.0f, 6.0f, 4.0f, 7.0f, 2.0f, 9.0f,
7.0f, 3.0f, 2.0f, 5.0f, 7.0f, 3.0f, 10.0f, 2.0f, 6.0f, 1.0f, 4.0f, 7.0f, 5.0f, 10.0f, 3.0f, 10.0f, 4.0f, 5.0f, 5.0f, 1.0f, 6.0f, 10.0f, 7.0f, 4.0f, 5.0f, 3.0f, 9.0f, 9.0f, 8.0f, 6.0f, 9.0f, 2.0f, 3.0f, 6.0f, 8.0f, 5.0f,
5.0f, 5.0f, 5.0f, 5.0f, 3.0f, 10.0f, 4.0f, 1.0f, 8.0f, 8.0f, 9.0f, 8.0f, 4.0f, 1.0f, 4.0f, 9.0f, 3.0f, 6.0f, 3.0f, 1.0f, 4.0f, 8.0f, 3.0f, 10.0f, 8.0f, 6.0f, 4.0f, 5.0f, 4.0f, 3.0f, 2.0f, 2.0f, 4.0f, 3.0f, 6.0f, 4.0f,
};
// matrix B (16 X 36)
float matrixB[N * K] = {
9.0f, 7.0f, 1.0f, 3.0f, 5.0f, 9.0f, 7.0f, 6.0f, 1.0f, 10.0f, 1.0f, 1.0f, 7.0f, 2.0f, 4.0f, 9.0f, 10.0f, 4.0f, 5.0f, 5.0f, 7.0f, 1.0f, 7.0f, 7.0f, 2.0f, 9.0f, 5.0f, 10.0f, 7.0f, 4.0f, 8.0f, 9.0f, 9.0f, 3.0f, 10.0f, 2.0f,
4.0f, 6.0f, 10.0f, 9.0f, 5.0f, 1.0f, 8.0f, 7.0f, 4.0f, 7.0f, 2.0f, 6.0f, 5.0f, 3.0f, 1.0f, 10.0f, 8.0f, 4.0f, 8.0f, 3.0f, 7.0f, 1.0f, 2.0f, 7.0f, 6.0f, 8.0f, 6.0f, 5.0f, 2.0f, 3.0f, 1.0f, 1.0f, 2.0f, 5.0f, 7.0f, 1.0f,
8.0f, 2.0f, 8.0f, 8.0f, 8.0f, 8.0f, 4.0f, 4.0f, 6.0f, 10.0f, 10.0f, 9.0f, 2.0f, 9.0f, 3.0f, 7.0f, 7.0f, 1.0f, 4.0f, 9.0f, 1.0f, 2.0f, 3.0f, 6.0f, 1.0f, 10.0f, 5.0f, 8.0f, 9.0f, 4.0f, 6.0f, 2.0f, 3.0f, 1.0f, 2.0f, 7.0f,
5.0f, 1.0f, 7.0f, 2.0f, 9.0f, 10.0f, 9.0f, 5.0f, 2.0f, 5.0f, 4.0f, 10.0f, 9.0f, 9.0f, 1.0f, 9.0f, 8.0f, 8.0f, 9.0f, 4.0f, 9.0f, 4.0f, 8.0f, 2.0f, 1.0f, 8.0f, 4.0f, 5.0f, 10.0f, 7.0f, 6.0f, 2.0f, 1.0f, 10.0f, 10.0f, 7.0f,
9.0f, 4.0f, 5.0f, 9.0f, 5.0f, 10.0f, 10.0f, 3.0f, 6.0f, 6.0f, 4.0f, 4.0f, 4.0f, 8.0f, 5.0f, 4.0f, 9.0f, 1.0f, 9.0f, 9.0f, 1.0f, 7.0f, 9.0f, 2.0f, 10.0f, 9.0f, 10.0f, 8.0f, 3.0f, 3.0f, 9.0f, 3.0f, 9.0f, 10.0f, 1.0f, 8.0f,
9.0f, 2.0f, 6.0f, 9.0f, 7.0f, 2.0f, 3.0f, 5.0f, 3.0f, 6.0f, 9.0f, 7.0f, 3.0f, 7.0f, 6.0f, 4.0f, 10.0f, 3.0f, 5.0f, 7.0f, 2.0f, 9.0f, 3.0f, 2.0f, 2.0f, 10.0f, 8.0f, 7.0f, 3.0f, 10.0f, 6.0f, 3.0f, 1.0f, 1.0f, 4.0f, 10.0f,
2.0f, 9.0f, 2.0f, 10.0f, 6.0f, 4.0f, 3.0f, 6.0f, 3.0f, 6.0f, 9.0f, 7.0f, 8.0f, 8.0f, 3.0f, 3.0f, 10.0f, 5.0f, 2.0f, 10.0f, 7.0f, 10.0f, 9.0f, 3.0f, 6.0f, 6.0f, 5.0f, 10.0f, 2.0f, 3.0f, 6.0f, 1.0f, 9.0f, 4.0f, 10.0f, 4.0f,
10.0f, 7.0f, 8.0f, 10.0f, 10.0f, 8.0f, 7.0f, 10.0f, 4.0f, 6.0f, 8.0f, 7.0f, 7.0f, 6.0f, 9.0f, 3.0f, 6.0f, 5.0f, 5.0f, 2.0f, 7.0f, 2.0f, 7.0f, 4.0f, 4.0f, 6.0f, 6.0f, 4.0f, 3.0f, 9.0f, 3.0f, 6.0f, 4.0f, 7.0f, 2.0f, 9.0f,
7.0f, 3.0f, 2.0f, 5.0f, 7.0f, 3.0f, 10.0f, 2.0f, 6.0f, 1.0f, 4.0f, 7.0f, 5.0f, 10.0f, 3.0f, 10.0f, 4.0f, 5.0f, 5.0f, 1.0f, 6.0f, 10.0f, 7.0f, 4.0f, 5.0f, 3.0f, 9.0f, 9.0f, 8.0f, 6.0f, 9.0f, 2.0f, 3.0f, 6.0f, 8.0f, 5.0f,
5.0f, 5.0f, 5.0f, 5.0f, 3.0f, 10.0f, 4.0f, 1.0f, 8.0f, 8.0f, 9.0f, 8.0f, 4.0f, 1.0f, 4.0f, 9.0f, 3.0f, 6.0f, 3.0f, 1.0f, 4.0f, 8.0f, 3.0f, 10.0f, 8.0f, 6.0f, 4.0f, 5.0f, 4.0f, 3.0f, 2.0f, 2.0f, 4.0f, 3.0f, 6.0f, 4.0f,
6.0f, 2.0f, 3.0f, 3.0f, 3.0f, 7.0f, 5.0f, 1.0f, 8.0f, 1.0f, 4.0f, 5.0f, 1.0f, 1.0f, 6.0f, 4.0f, 2.0f, 1.0f, 7.0f, 8.0f, 6.0f, 1.0f, 1.0f, 5.0f, 6.0f, 5.0f, 10.0f, 6.0f, 7.0f, 5.0f, 9.0f, 3.0f, 2.0f, 7.0f, 9.0f, 4.0f,
2.0f, 5.0f, 9.0f, 5.0f, 10.0f, 3.0f, 1.0f, 8.0f, 1.0f, 7.0f, 1.0f, 8.0f, 1.0f, 6.0f, 7.0f, 8.0f, 4.0f, 9.0f, 5.0f, 10.0f, 3.0f, 7.0f, 6.0f, 8.0f, 8.0f, 5.0f, 6.0f, 8.0f, 10.0f, 9.0f, 4.0f, 1.0f, 3.0f, 3.0f, 4.0f, 7.0f,
8.0f, 2.0f, 6.0f, 6.0f, 5.0f, 1.0f, 3.0f, 7.0f, 1.0f, 7.0f, 2.0f, 2.0f, 2.0f, 8.0f, 4.0f, 1.0f, 1.0f, 5.0f, 9.0f, 4.0f, 1.0f, 2.0f, 3.0f, 10.0f, 1.0f, 4.0f, 9.0f, 9.0f, 6.0f, 8.0f, 8.0f, 1.0f, 9.0f, 10.0f, 4.0f, 1.0f,
8.0f, 5.0f, 8.0f, 9.0f, 4.0f, 8.0f, 2.0f, 1.0f, 1.0f, 9.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 5.0f, 6.0f, 7.0f, 3.0f, 1.0f, 4.0f, 6.0f, 7.0f, 7.0f, 7.0f, 8.0f, 7.0f, 8.0f, 8.0f, 2.0f, 10.0f, 2.0f, 7.0f, 3.0f, 8.0f, 3.0f,
8.0f, 7.0f, 6.0f, 2.0f, 4.0f, 10.0f, 10.0f, 6.0f, 10.0f, 3.0f, 7.0f, 6.0f, 4.0f, 3.0f, 5.0f, 5.0f, 5.0f, 3.0f, 8.0f, 10.0f, 3.0f, 4.0f, 8.0f, 4.0f, 2.0f, 6.0f, 8.0f, 9.0f, 6.0f, 9.0f, 4.0f, 3.0f, 5.0f, 2.0f, 2.0f, 6.0f,
10.0f, 6.0f, 2.0f, 1.0f, 7.0f, 5.0f, 6.0f, 4.0f, 1.0f, 9.0f, 10.0f, 2.0f, 4.0f, 5.0f, 8.0f, 5.0f, 7.0f, 4.0f, 7.0f, 6.0f, 3.0f, 9.0f, 2.0f, 1.0f, 4.0f, 2.0f, 6.0f, 6.0f, 3.0f, 3.0f, 2.0f, 8.0f, 5.0f, 9.0f, 3.0f, 4.0f,
};
// matrix C (4 x 16)
float expected_result[M * N] = {
1224.0f, 1023.0f, 1158.0f,1259.0f,1359.0f,1194.0f,1535.0f,1247.0f,1185.0f,1029.0f,889.0f,1182.0f,955.0f,1179.0f,1147.0f,1048.0f,
1216.0f, 1087.0f, 1239.0f,1361.0f,1392.0f,1260.0f,1247.0f,1563.0f,1167.0f,1052.0f,942.0f,1214.0f,1045.0f,1134.0f,1264.0f,1126.0f,
1125.0f, 966.0f, 1079.0f,1333.0f,1287.0f,1101.0f,1185.0f,1167.0f,1368.0f,990.0f,967.0f,1121.0f,971.0f,1086.0f,1130.0f,980.0f,
999.0f, 902.0f, 1020.0f,1056.0f,1076.0f,929.0f,1029.0f,1052.0f,990.0f,1108.0f,823.0f,989.0f,759.0f,1041.0f,1003.0f,870.0f
};
bool passed = true;
perform_gemm_test(matrixA, matrixB, expected_result, M, N, K);
test_model model;
load_model(model, matrixA, matrixB, M, N, K, true);
ggml_gallocr_t allocr = NULL;
{
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
//create the worst case graph for memory usage estimation
struct ggml_cgraph * gf = build_graph(model);
// compute the required memory
ggml_gallocr_reserve(allocr, gf);
size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f);
}
struct ggml_tensor * result = compute(model, allocr);
float* out_data = new float[ggml_nelements(result)];
ggml_backend_tensor_get(result, out_data, 0, ggml_nbytes(result));
printf("\nPerforming ggml_mul_mat test:\n");
passed = true;
for(int i = 0; i < M * N; i++) {
if(out_data[i] != expected_result[i]) {
passed = false;
break;
}
}
for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
printf("%.1f ", out_data[i * N + j]);
}
printf("\n");
}
printf("ggml_mul_mat (%d): %s\n", (int) ggml_nelements(result), passed && (ggml_nelements(result) == M * N) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
// free memory
ggml_free(model.ctx);
ggml_backend_buffer_free(model.buffer);
ggml_backend_free(model.backend);
ggml_gallocr_free(allocr);
return 0;
}